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More detailed description of benefits can be found at #41001. This is Intel's counterpart of NVidia’s NVTX (https://pytorch.org/docs/stable/autograd.html#torch.autograd.profiler.emit_nvtx). ITT is a functionality for labeling trace data during application execution across different Intel tools. For integrating Intel(R) VTune Profiler into Kineto, ITT needs to be integrated into PyTorch first. It works with both standalone VTune Profiler [(https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html](https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html)) and Kineto-integrated VTune functionality in the future. It works for both Intel CPU and Intel XPU devices. Pitch Add VTune Profiler's ITT API function calls to annotate PyTorch ops, as well as developer customized code scopes on CPU, like NVTX for NVidia GPU. This PR rebases the code changes at https://github.com/pytorch/pytorch/pull/61335 to the latest master branch. Usage example: ``` with torch.autograd.profiler.emit_itt(): for i in range(10): torch.itt.range_push('step_{}'.format(i)) model(input) torch.itt.range_pop() ``` cc @ilia-cher @robieta @chaekit @gdankel @bitfort @ngimel @orionr @nbcsm @guotuofeng @guyang3532 @gaoteng-git Pull Request resolved: https://github.com/pytorch/pytorch/pull/63289 Approved by: https://github.com/malfet
130 lines
4.1 KiB
Bash
Executable File
130 lines
4.1 KiB
Bash
Executable File
#!/usr/bin/env bash
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##############################################################################
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# Example command to build the Tizen target (RPi3).
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##############################################################################
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#
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# This script shows how one can build a Caffe2 binary for a Tizen device (RPi3).
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# The build is essentially much similar to a host build, with one additional change
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# which is to specify -mfpu=neon for optimized speed.
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setup_environment(){
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# The rootfs image for a Tizen target (RPi3)is located at the below webpage:
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# http://download.tizen.org/releases/milestone/tizen/4.0.m1/tizen-unified_20170529.1/images/
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# If you do not have a Tizen device, Please, run qemu-arm-static and chroot command.
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# $ sudo chroot ~/tizen-rootfs qemu-arm-static /usr/bin/bash
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CAFFE2_ROOT="$( cd "$(dirname -- "$0")"/.. ; pwd -P)"
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echo "Caffe2 codebase root is: $CAFFE2_ROOT"
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BUILD_ROOT=${BUILD_ROOT:-"$CAFFE2_ROOT/build"}
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mkdir -p $BUILD_ROOT
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echo "Build Caffe2 Tizen into: $BUILD_ROOT"
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}
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caffe2_lite_dep_packages(){
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# Obtain necessary dependencies
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# You can set-up a rpm repository with zypper, yum, and dnf because Tizen
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# software platform officially support rpm format such as Fedora, OpenSUSE.
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# The official Tizen repository is as following:
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# http://download.tizen.org/releases/milestone/tizen/4.0.m1/
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echo "Installing dependencies."
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sudo zypper install \
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make \
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strace \
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cmake \
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gcc* \
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binutils \
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glibc* \
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cpp \
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protobuf-devel \
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libstdc++*
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}
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caffe2_lite_build(){
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# Now, actually build the android target.
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echo "Building caffe2"
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cd $BUILD_ROOT
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# Note: add more dependencies above if you need libraries such as leveldb, lmdb, etc.
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# If you have to disable a specific package due to a package absence
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# from https://git.tizen.org/cgit/, append -Dxxx_xxx=OFF option before executing cmake.
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cmake .. \
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-DCMAKE_VERBOSE_MAKEFILE=1 \
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-DUSE_GFLAGS=OFF \
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-DUSE_GLOG=OFF -DUSE_NNPACK=OFF \
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-DRUN_HAVE_STD_REGEX=0 \
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-DRUN_HAVE_POSIX_REGEX=0 \
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-DHAVE_GNU_POSIX_REGEX=0 \
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-DUSE_MPI=OFF -DUSE_OPENMP=OFF \
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-DUSE_ROCKSDB=OFF \
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-DUSE_LEVELDB=OFF \
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-DUSE_LMDB=OFF \
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-DBUILD_PYTHON=OFF \
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-DUSE_GLOO=OFF \
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-DUSE_OPENCV=OFF \
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-DCAFFE2_CPU_FLAGS="-mfpu=neon -mfloat-abi=soft" \
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|| exit 1
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make -j`nproc` || exit 1
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}
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caffe2_full_dep_packages(){
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# Obtain necessary dependencies
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# You can set-up a rpm repository with zypper, yum, and dnf because Tizen
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# software platform officially support rpm format such as Fedora, OpenSUSE.
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# The official Tizen repository is as following:
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# http://download.tizen.org/releases/milestone/tizen/4.0.m1/
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echo "Installing dependencies."
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sudo zypper install \
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cmake \
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libgflags-dev \
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libgoogle-glog-dev \
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libprotobuf-dev \
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protobuf-compiler
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# Obtain optional dependencies that are usually useful to have.
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echo "Installing optional dependencies."
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sudo zypper install \
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libleveldb-dev \
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liblmdb-dev \
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libpython-dev \
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libsnappy-dev \
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python-numpy \
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python-pip \
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python-protobuf
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# Obtain python hypothesis, which Caffe2 uses for unit testing. Note that
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# the one provided by zypper is quite old so we install it via pip
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sudo pip install hypothesis
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# Install the six module, which includes Python 2 and 3 compatibility utilities,
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# and is required for Caffe2
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sudo pip install six
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}
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caffe2_full_build(){
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# Now, actually build the android target.
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echo "Building caffe2"
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cd $BUILD_ROOT
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# Note: add more dependencies above if you need libraries such as leveldb, lmdb, etc.
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# If you have to disable a specific package due to a package absence
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# from https://git.tizen.org/cgit/, append -Dxxx_xxx=OFF option before executing cmake.
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cmake "$CAFFE2_ROOT" \
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-DCMAKE_VERBOSE_MAKEFILE=1 \
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-DUSE_CUDA=OFF \
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-DUSE_ITT=OFF \
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-DUSE_OPENCV=OFF \
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-DUSE_LMDB=OFF \
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-DCAFFE2_CPU_FLAGS="-mfpu=neon -mfloat-abi=soft" \
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|| exit 1
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make -j`nproc` || exit 1
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}
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#### Main
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# Setup a build environment to compile Caffe2 deeplearning framework in Tizen platform.
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setup_environment
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# There are two build options to support 'full' version and 'lite' version (by default).
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caffe2_lite_dep_packages
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caffe2_lite_build
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